Regulatory Fragmentation: Figures

Preparation

Load Packages

This code loads packages used in R and provide a path to data folder.

### install.package(pacman)
require(pacman)
p_load(ggplot2, data.table, lubridate, ggthemes, dplyr, RColorBrewer, rmarkdown, randomcoloR, 
       knitr, psych, wordcloud, kableExtra, tidyverse, flextable, scales)

### path to files 
data_path <- "/Users/evolkova/Dropbox/Projects/Govt Agenda/Data/"

Create Labels for Common Agencies

This chunk of code creates abbreviations for names of the key agencies.

mapagency <- data.table(agency = "environmental protection agency", label = "EPA") %>%
  rbind(list("department of health and human services", "DHHS")) %>%
  rbind(list("department of transportation", "Transportation")) %>%
  rbind(list("department of commerce", "Commerce")) %>%
  rbind(list("securities and exchange commission", "SEC")) %>%
  rbind(list("department of interior", "DOI")) %>%
  rbind(list("department of agriculture", "USDA")) %>%
  rbind(list("department of energy", "DOE")) %>%
  rbind(list("department of labor", "DOL")) %>%
  rbind(list("department of treasury", "Treasury")) %>%
  rbind(list("department of justice", "DOJ")) %>%
  rbind(list("nuclear regulatory commission", "NRC")) %>%
  rbind(list("federal communications commission", "FCC")) %>%
  rbind(list("department of housing and urban developm", "HUD")) %>%
  rbind(list("federal reserve system", "FRS")) %>%
  rbind(list("international trade commission", "ITC")) %>%
  rbind(list("federal energy regulatory commission", "FERC")) %>%
  rbind(list("commodity futures trading commission", "CFTC")) %>%
  rbind(list("department of veteran affairs", "DVA")) %>%
  rbind(list("consumer financial protection bureau", "CFPB")) %>%
  rbind(list("department of defense", "Defense")) %>%
  rbind(list("social security administration", "SSA"))

setkey(mapagency, agency)


mapagency %>% 
  flextable() %>% 
  autofit() %>% 
  theme_booktabs()

agency	label
commodity futures trading commission	CFTC
consumer financial protection bureau	CFPB
department of agriculture	USDA
department of commerce	Commerce
department of defense	Defense
department of energy	DOE
department of health and human services	DHHS
department of housing and urban developm	HUD
department of interior	DOI
department of justice	DOJ
department of labor	DOL
department of transportation	Transportation
department of treasury	Treasury
department of veteran affairs	DVA
environmental protection agency	EPA
federal communications commission	FCC
federal energy regulatory commission	FERC
federal reserve system	FRS
international trade commission	ITC
nuclear regulatory commission	NRC
securities and exchange commission	SEC
social security administration	SSA

Load Datasets

### this file includes top-100 words per topic
### it is a list of 100 tables 
### PROVIDED in the replication package
wordcloudlist <- "wordcloudlist.rds" %>%
  paste0(data_path, .) %>%
  readRDS()

wordcloudlist[[1]] %>% 
  head(20) %>% 
  flextable() %>% 
  autofit()

topic	term	beta
1	fee	0.109794177
1	exchang	0.033467054
1	market	0.022850266
1	order	0.021793327
1	tier	0.019684188
1	charg	0.019139130
1	pilot	0.018887929
1	propos	0.014873456
1	per	0.014854498
1	execut	0.013977665
1	rebat	0.013944487
1	will	0.013655370
1	custom	0.013271459
1	member	0.012944424
1	liquid	0.012669525
1	schedul	0.012636348
1	option	0.011816390
1	rout	0.009171673
1	particip	0.009015264
1	month	0.008792502

### this file includes labels of all 100 topics
### PROVIDED in the replication package
labels <- "topic_labels_ML_KV_JK.csv" %>%
  paste0(data_path, .) %>%
  fread(select = c("ShortLabel", "Topic"))

labels %>% 
  head(10) %>% 
  flextable() %>% 
  autofit()

ShortLabel	Topic
Penalties: Freedom of Information	1
Penalties: banks	2
Endangered Species	3
EBP	4
Securities: invt cos	5
Grants: housing & community devt	6
Env: data & studies	7
Veterans	8
Government employees	9
EP (agriculture)	10

### this is the main file
### SYNTHETIC version is included in the replication package
companyyear <- "companyyear_final.rds" %>%
  paste0(data_path, .) %>%
  readRDS() %>% 
  select("cik", "name", "year", "FF12", "regul.disp", "topic.disp", "Topic29")

companyyear %>% 
  head(10) %>% 
  flextable() %>% 
  autofit()

cik	name	year	FF12	regul.disp	topic.disp	Topic29
20	K TRON INTERNATIONAL INC	1,996	6	0.8399150	0.9274241	0.00055921461
20	K TRON INTERNATIONAL INC	1,997	6	0.8522455	0.9276763	0.00100173634
20	K TRON INTERNATIONAL INC	1,999	6	0.8283158	0.9321834	0.00006559097
20	K TRON INTERNATIONAL INC	2,001	6	0.8484812	0.9339354	0.00006591958
20	K TRON INTERNATIONAL INC	2,003	6	0.8347048	0.9406899	0.00004764173
20	K TRON INTERNATIONAL INC	2,004	6	0.8093653	0.9400247	0.00043147407
20	K TRON INTERNATIONAL INC	2,005	6	0.8144986	0.9404953	0.00734362186
20	K TRON INTERNATIONAL INC	2,006	6	0.8025276	0.9428131	0.01438246432
20	K TRON INTERNATIONAL INC	2,007	6	0.7887117	0.9455084	0.00580932147
20	K TRON INTERNATIONAL INC	2,008	6	0.8043702	0.9425870	0.00396724641

### this file shows topic distribution over agency-years
### PROVIDED in the replication package
topicagencyyear <- "topicagencyyear.csv" %>%
  paste0(data_path, .) %>%
  fread()

topicagencyyear %>% 
  head(10) %>% 
  flextable() %>% 
  autofit()

TopicNumber	agency	year	TopicWords	AllWords	AgencyPercent
1	securities and exchange commission	1,995	6,447.6060	52,772.61	0.122177134
1	department of agriculture	1,995	3,694.0364	52,772.61	0.069999126
1	environmental protection agency	1,995	4,929.8400	52,772.61	0.093416645
1	federal communications commission	1,995	3,835.1592	52,772.61	0.072673293
1	department of justice	1,995	2,713.3791	52,772.61	0.051416431
1	commodity credit corporation	1,995	100.6776	52,772.61	0.001907762
1	general services administration	1,995	185.5532	52,772.61	0.003516090
1	department of health and human services	1,995	2,890.8016	52,772.61	0.054778450
1	department of treasury	1,995	1,413.3309	52,772.61	0.026781525
1	national credit union administration	1,995	106.9971	52,772.61	0.002027512

### this file include data obtained from Federal Register
### PROVIDED in the replication package
fedreg <- "fedreg_with_words.csv" %>%
  paste0(data_path, .) %>%
  fread()

fedreg %>% 
  head(10) %>% 
  flextable() %>% 
  autofit()

document_number	type	date	year	agency	Nagencies	nwords
94-32388	Presidential Document	1995-01-03	1,995	executive office of the president	1	174
94-32287	Rule	1995-01-03	1,995	department of agriculture	1	366
94-32288	Rule	1995-01-03	1,995	department of agriculture	1	288
94-32036	Rule	1995-01-03	1,995	department of transportation	1	351
94-32250	Rule	1995-01-03	1,995	securities and exchange commission	1	239
94-32126	Rule	1995-01-03	1,995	international trade commission	1	2,679
94-32127	Rule	1995-01-03	1,995	international trade commission	1	1,334
94-32331	Rule	1995-01-03	1,995	department of treasury	1	2,237
94-31665	Rule	1995-01-03	1,995	department of treasury	1	1,041
94-32197	Rule	1995-01-03	1,995	department of justice	1	120

### this file include results of placebo test
### PROVIDED in the replication package
### Code for random simulations is also provided

firm_placebo <- "firm_placebo.csv" %>% 
  paste0(data_path,.) %>% 
  fread

firm_placebo %>% 
  head(10) %>% 
  flextable() %>% 
  autofit()

Estimate	Std. Error	t value	Pr(>|t|)	variable	yvar	iter
0.055692551	0.014080436	3.9553144	0.000256366381497640991299	regul.disp_norm	lead.sga_at_norm	0
0.033443098	0.025282297	1.3227872	0.192306446873755004789430	regul.complex.log_norm	lead.sga_at_norm	0
0.045634481	0.018924892	2.4113469	0.019854553478668199306689	topic.disp_norm	lead.sga_at_norm	0
-0.014225601	0.011128463	-1.2783078	0.207417814681061019044250	LogWords10K_norm	lead.sga_at_norm	0
0.068140415	0.030279498	2.2503812	0.029144311243643600956021	ppe_at_norm	lead.sga_at_norm	0
-0.023155087	0.009721726	-2.3817876	0.021329156235213701103826	ebitda_at_norm	lead.sga_at_norm	0
-0.317033602	0.049643457	-6.3862112	0.000000069891455211498988	log_sale_norm	lead.sga_at_norm	0
0.110807761	0.008201900	13.5100115	0.000000000000000008434914	tobin_norm	lead.sga_at_norm	0
-0.030862211	0.017811584	-1.7327045	0.090315831480421007726456	regul.disp_norm	lead.tfp_norm	0
0.006716982	0.020473255	0.3280857	0.744439126311973065064365	regul.complex.log_norm	lead.tfp_norm	0

Figure 1: Total number of words in the Federal Register

data <- fedreg[year %in% 1995:2019 & type %in% c("Notice", "Proposed Rule", "Rule"), list(Words = sum(nwords)), by = c("year,type")]

library(ggplot2)

plot <- ggplot(data, aes(x = year, y = Words, col = type)) + 
  geom_line() +
  ylab("Words in Federal Register") + 
  theme_minimal() + 
  theme(axis.title.x = element_blank()) +
  scale_color_brewer(palette = "Paired") +
  scale_y_continuous(labels = comma,limits = c(0, 8*10^6)) + 
  guides(color = guide_legend(title = "Document Type", ncol = 3)) + 
  theme(legend.position='bottom') 

      
print(plot)

Figure 2: Topic clouds

make_cloud <- function(i) {
  wordcloud(wordcloudlist[[i]]$term, wordcloudlist[[i]]$beta, colors = brewer.pal(8, "Dark2"), random.order = F) %>%
    print()
}
make_cloud(3)

## NULL

make_cloud(5)

## NULL

make_cloud(21)

## NULL

make_cloud(23)

## NULL

Figure 3: Regulatory fragmentation histograms

Panel A: Fragmentation of topics across agencies

data <- topicagencyyear[, list(disp = 1 - sum(AgencyPercent^2)), by = c("year,TopicNumber")]

p <- ggplot(data = data, aes(disp)) +
  geom_histogram(aes(y = 0.01 * ..density..), fill = "darkgreen", binwidth = 0.01, color = "black") +
  theme_minimal() +
  xlab("Dispersion of Topics Across Agencies") +
  ylab("Density [% of topic-year obs]")

print(p)

Panel B: Dispersion of topics within each firm-year

p <- ggplot(data = companyyear, aes(topic.disp)) +
  geom_histogram(aes(y=0.0025*..density..),fill="steelblue", col = "darkblue", bins = 30) + 
  theme_minimal() + xlab("Dispersion of Topics Within Each Firm-Year") + 
  ylab("Density [% of Firm-Year Obs]")

print(p)

Panel C: Regulatory fragmentation at the firm-year level

p <- ggplot(data = companyyear, aes(regul.disp)) +
  geom_histogram(aes(y = 0.005 * ..density..), fill = "purple", binwidth = 0.005, color = "purple4") +
  theme_minimal() +
  xlab("Regulatory Fragmentation") +
  ylab("Density [% of firm-year obs]")
print(p)

Figure 4: Time trends in regulatory fragmentation

Panel A: 12 Fama-French Industries

### generate palette with 12 colors
pl <- distinctColorPalette(12)

## create labels for FF12 industries
labs <- data.table(ind = 1:12, lab = c("1 NoDur", "2 Durbl", "3 Manuf", "4 Enrgy ", "5 Chems", "6 BusEq", "7 Telcm", "8 Utils", "9 Shops", "10 Hlth", "11 Money", "12 Other"))

## add labels to company-year file
companyyear$FF12_lab <- factor(labs[match(companyyear$FF12, labs$ind)]$lab, levels = labs$lab)

companyyear[, mean(regul.disp), by = "FF12_lab,year"] |>
  ggplot(aes(y = V1, x = year, col = FF12_lab, group = FF12_lab)) +
  geom_line() +
  theme_minimal() +
  theme(
    axis.line = element_line(size = rel(1), colour = "black"),
    panel.grid.major = element_line(colour = "#d3d3d3"),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(), panel.background = element_blank(),
    plot.title = element_text(size = rel(1.5), face = "bold"),
    axis.text.x = element_text(colour = "black", size = rel(1.3)),
    axis.title.x = element_text(colour = "black", size = rel(1.3)),
    axis.title.y = element_text(colour = "black", size = rel(1.3)),
    axis.text.y = element_text(colour = "black", size = rel(1.4))
  ) +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab("Year") +
  ylab("Regulatory Fragmentation") +
  scale_color_manual(values = pl, name = "Industry") +
  ylim(0.72, 0.88)

Panel B: Fama-French Industry #1 (Non-Durables)

pl <- distinctColorPalette(length(unique(companyyear[FF12 == 1]$cik)))

companyyear[FF12 == 1] |>
  ggplot(aes(y = regul.disp, x = year, col = as.character(cik), group = cik)) +
  geom_line(alpha = 0.5, size = 0.5) +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(
    axis.line = element_line(size = rel(1), colour = "black"),
    panel.grid.major = element_line(colour = "#d3d3d3"),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(), panel.background = element_blank(),
    plot.title = element_text(size = rel(1.5), face = "bold"),
    axis.text.x = element_text(colour = "black", size = rel(1.3)),
    axis.title.x = element_text(colour = "black", size = rel(1.3)),
    axis.title.y = element_text(colour = "black", size = rel(1.3)),
    axis.text.y = element_text(colour = "black", size = rel(1.4))
  ) +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab("Year") +
  ylab("Regulatory Fragmentation") +
  scale_color_manual(values = pl) +
  ylim(0.72, 0.88)

Figure 5: Changes in Regulatory Fragmentation around Trump Election and the Global Financial Crisis

Panel A: Topics with the biggest change in fragmentation around the 2016 presidential election

difgraph <- function(int1, int2, lab1, lab2) {
  
  ### generate agency dispersion file 
  agencydisp <- topicagencyyear[, list(AgencyDisp = 1 - sum(AgencyPercent^2)), by = "TopicNumber,year"]
  
  ### measure interval1 and interval2 dispersions by topic
  increases <- agencydisp[, list(
    regul1 = mean(AgencyDisp[year %in% int1]), 
    regul2 = mean(AgencyDisp[year %in% int2])), by = TopicNumber]

  ### estimate change between interval2 and interval1
  increases[, change := regul2 - regul1]
  
  ### order observations by change
  setkey(increases, change)
  
  ### add labels to data.table with increases
  for(tp in as.numeric(1:100)) increases[TopicNumber == tp, label1 := labels$ShortLabel[match(tp, labels$Topic)]]
  
  ### add topicnumber to the start of the label 
  increases[, label1 := paste0(TopicNumber, ".", label1)]
  
  ### rank all observations (they are ordered by change)
  increases[, rank := 1:.N]
  
  ### split label into several lines, 15 characters each
  increases[, label1 := paste0(strsplit(as.character(label1), "(?<=.{15})", perl = TRUE)[[1]], collapse = "\n"), 
            by = rank]

  ### look at top and bottom 3 topics by change
  increases <- increases[rank %in% c(1:3, 98:100)]
  
  ### set layers (they will determine the order)
  layers <- increases$label[c(6:4, 1:3)]
  
  ### set rank as data.table key
  setkey(increases, rank)
  
  ### select variables and melt into longer data.table (for ggplot2)
  increases <- increases %>%
    select("TopicNumber", "label1", "regul1", "regul2", "rank") %>%
    melt(id.vars = c("TopicNumber", "label1", "rank"))

  ### rename columns
  colnames(increases) <- c("topic", "label", "rank", "year", "reguldisp")

  ### divide between increases and decreases 
  increases[rank %in% 1:3, type := "2. Decrease"]
  increases[rank %in% 98:100, type := "1. Increase"]
  
  ### put labels to word count before/after
  increases[year == "regul1", year := lab1]
  increases[year == "regul2", year := lab2]

  ### add layers (determine the order)
  increases$label <- factor(increases$label, levels = layers)

  ### plot graph set up
  plot <- increases %>%
    ggplot(aes(x = label, y = reguldisp, fill = year)) +
    geom_bar(stat = "identity", position = "dodge") +
    facet_wrap(~type, scales = "free_x") +
    theme_minimal() +
    theme(legend.position='bottom', text=element_text(family="Georgia"),
          legend.title = element_text(size = rel(0.75)), 
          legend.text = element_text(size = rel(0.75)),
          axis.title.x = element_blank()) +
    guides(fill = guide_legend(title = "Period")) +
    scale_fill_brewer(palette = "Paired") +
    ylab("Dispersion Across Agencies")

  return(plot)
}

difgraph(2014:2016, 2017:2019, "2014-2016", "2017-2019") %>% print()

Panel B: Topics with the biggest change in fragmentation around the 2008-09 financial crisis

difgraph(2006:2008, 2009:2011, "2006-2008","2009-2011") %>% print

Figure 6: A case study

Panel A: # words written on ‘banks’ topic each year, by the top 10 agencies

top_agencies <- function(years, TP) {
  ### set colors
  my_palette <- c(
    "#E74C3C", "#3498DB", "#2ECC71", "#9B59B6", "#F1C40F",
    "#E67E22", "#1ABC9C", "#000000", "#C0392B", "#2980B9",
    "#ade5e5", "#8E44AD"
  )

  ### sort data
  sorted_data <- topicagencyyear[order(-topicagencyyear$AgencyPercent), ]

  ### select top-10 agencies based on sorting
  top_10_agencies <- sorted_data[year == min(years) & TopicNumber == TP, agency][1:10]

  ### select years and topic from topicagencyyear table
  data <- topicagencyyear[year %in% years & TopicNumber == TP]

  ### create shorter names for agencies using mapagency table
  data$Agency <- mapagency$label[match(data$agency, mapagency$agency)]

  ### some agency names are short and we can use them as labels
  data[is.na(Agency), Agency := agency]

  ### limit sample to top10 agencies
  data <- data[agency %in% top_10_agencies]

  ### this is the way how we can set an order to the legend in ggplot2
  data$Agency <- factor(data$Agency, levels = unique(data[match(top_10_agencies, data$agency), ]$Agency))


  ### plot the graph
  pl <- ggplot(data, aes(x = Agency, y = TopicWords, fill = Agency)) +
    geom_bar(stat = "identity") +
    facet_wrap(~year) +
    scale_fill_manual(values = my_palette) +
    scale_y_continuous(labels = comma) +
    ylab("Words written by an agency on selected topic") +
    theme(
      axis.text.x = element_blank(),
      axis.ticks.x = element_blank(),
      legend.position = "bottom",
      axis.line = element_line(size = rel(1), colour = "black"),
      panel.grid.major = element_line(colour = "#d3d3d3"),
      panel.grid.minor = element_blank(),
      panel.border = element_blank(),
      panel.background = element_blank(),
      plot.title = element_text(size = rel(1.5), face = "bold", hjust = 0.5),
      axis.title.y = element_text(colour = "black", size = rel(1.2)),
      axis.text.y = element_text(colour = "black", size = rel(1.2))
    )
  return(pl)
}

top_agencies(2014:2019, 29)

Panel B: Regulatory fragmentation each year, of 12 firms with highest weight on ‘banks’ topic

plot_top_companies <- function(years, TP, shockyr) {
  my_palette <- c(
    "#E74C3C", "#3498DB", "#2ECC71", "#9B59B6", "#F1C40F",
    "#E67E22", "#1ABC9C", "#000000", "#C0392B", "#2980B9",
    "#ade5e5", "#8E44AD", "#ade5e5", "#8E44AD"
  )

  ### select years and variables from the main file
  data <- companyyear[year %in% years] %>% select("cik", "year", "name", TP, "regul.disp")

  ### order data
  setkey(data, cik, year)

  ### count number of observations per CIK
  data[, n1 := .N, by = cik]

  ### drop companies with less than 5 obs (IPOs or exits)
  data <- data[n1 > 5]

  ### shorter the name of the company, make it robust to the name change
  data[, name := substr((name[1]), 1, 35), by = cik]

  ### set order by the frequency of selected topic
  data <- data[order(data[[4]], decreasing = T)]

  ### number companies by the frequency of the key topic per year
  data[, n2 := 1:.N, by = "year"]

  ### order by company and year
  setkey(data, name, year)

  ### fix order of the companies at the start of the sample
  ### (i.e. company #1 is company with higher weight on TP topic)
  data[, n3 := n2[1], by = name]

  ### keep top12 companies (based on the topic frequency in the first year)
  data <- data[n3 %in% 1:12]

  ### this is the way company names will be ordered in the legend
  data$name <- factor(data$name, levels = unique(data[order(n3)]$name))

  ### plot the graph
  pl <- data %>%
    ggplot(aes(x = year, y = regul.disp, group = name, col = name, fill = name)) +
    geom_line(alpha = 0.7, size = 1.5) +
    theme(legend.position = "bottom") +
    theme(
      axis.line = element_line(size = rel(1), colour = "black"),
      panel.grid.major = element_line(colour = "#d3d3d3"),
      panel.grid.minor = element_blank(),
      panel.border = element_blank(), panel.background = element_blank(),
      plot.title = element_text(size = rel(1.5), face = "bold"),
      axis.text.x = element_text(colour = "black", size = rel(1.0)),
      axis.title.x = element_text(colour = "black", size = rel(1)),
      legend.title = element_blank(),
      legend.text = element_text(size = rel(0.6)),
      axis.title.y = element_text(colour = "black", size = rel(1.2))
    ) +
    theme(plot.title = element_text(hjust = 0.5)) +
    xlab("Year") +
    ylab("Regulatory Fragmentation") +
    scale_color_manual(values = my_palette, name = "Company Name") +
    ylim(0.70, 0.85) +
    guides(col = guide_legend(title.position = "left", label.position = "right", ncol = 5)) +
    geom_vline(xintercept = shockyr, linetype = "dashed", color = "red")
  return(pl)
}

plot_top_companies(2014:2019, "Topic29", 2016) %>% print()

Figure 7: Placebo test to address endogeneity concerns

Panel A: Dept var = SG&A / assets

yvars <- c("lead.sga_at", "lead.tfp", "lead.roa", "lead.growth", "lead.growth.at", "lead.emp_at") %>% paste0("_norm")

### we plot histogram of t-statistics
### iteration zero = actual results (red line)
### other iterations = placebo (light blue bars)
pl <- function(y, fl = "lightblue"){
  data <- firm_placebo[yvar == y & variable == "regul.disp_norm"]
  
  ggplot(data = data[iter > 0]) + 
  geom_histogram(aes(`t value`), bins = 50, color="#000099", fill= fl) +
  theme_minimal() + xlab("T-statistics") + ylab("Count") + 
  geom_vline(xintercept = data[iter == 0]$`t value`, col = "red")
  
}

pl(yvars[1])

Panel B: Dept var = TFP

pl(yvars[2])

Panel C: Dept var = ROA

pl(yvars[3])

Panel D: Dept var = Sales growth

pl(yvars[4])

Panel E: Dept var = Assets growth

pl(yvars[5])

Panel F: Dept var = Employment

pl(yvars[6])

Session Info

sessionInfo()

## R version 4.2.2 (2022-10-31)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS 14.0
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] scales_1.2.1        flextable_0.9.0     forcats_1.0.0      
##  [4] stringr_1.5.0       purrr_1.0.1         readr_2.1.4        
##  [7] tidyr_1.3.0         tibble_3.1.8        tidyverse_2.0.0    
## [10] kableExtra_1.3.4    wordcloud_2.6       psych_2.3.3        
## [13] knitr_1.42          randomcoloR_1.1.0.1 rmarkdown_2.20     
## [16] RColorBrewer_1.1-3  dplyr_1.1.0         ggthemes_4.2.4     
## [19] lubridate_1.9.2     data.table_1.14.8   ggplot2_3.4.1      
## [22] pacman_0.5.1       
## 
## loaded via a namespace (and not attached):
##  [1] nlme_3.1-160            fontquiver_0.2.1        webshot_0.5.4          
##  [4] httr_1.4.7              tools_4.2.2             bslib_0.4.2            
##  [7] utf8_1.2.3              R6_2.5.1                colorspace_2.1-0       
## [10] withr_2.5.0             tidyselect_1.2.0        mnormt_2.1.1           
## [13] curl_5.0.2              compiler_4.2.2          textshaping_0.3.6      
## [16] cli_3.6.0               rvest_1.0.3             xml2_1.3.3             
## [19] officer_0.6.1           fontBitstreamVera_0.1.1 labeling_0.4.2         
## [22] triebeard_0.4.1         sass_0.4.5              askpass_1.1            
## [25] systemfonts_1.0.4       digest_0.6.31           svglite_2.1.1          
## [28] gfonts_0.2.0            pkgconfig_2.0.3         htmltools_0.5.4        
## [31] highr_0.10              fastmap_1.1.1           rlang_1.0.6            
## [34] rstudioapi_0.14         httpcode_0.3.0          shiny_1.7.4            
## [37] farver_2.1.1            jquerylib_0.1.4         generics_0.1.3         
## [40] jsonlite_1.8.7          zip_2.2.2               magrittr_2.0.3         
## [43] Rcpp_1.0.10             munsell_0.5.0           fansi_1.0.4            
## [46] gdtools_0.3.2           lifecycle_1.0.3         stringi_1.7.12         
## [49] yaml_2.3.7              Rtsne_0.16              grid_4.2.2             
## [52] parallel_4.2.2          promises_1.2.0.1        crayon_1.5.2           
## [55] lattice_0.20-45         hms_1.1.2               pillar_1.8.1           
## [58] uuid_1.1-0              crul_1.3                glue_1.6.2             
## [61] evaluate_0.20           V8_4.2.2                fontLiberation_0.1.0   
## [64] urltools_1.7.3          vctrs_0.5.2             tzdb_0.3.0             
## [67] httpuv_1.6.9            openssl_2.0.5           gtable_0.3.1           
## [70] cachem_1.0.7            xfun_0.37               mime_0.12              
## [73] xtable_1.8-4            later_1.3.0             ragg_1.2.5             
## [76] viridisLite_0.4.1       memoise_2.0.1           cluster_2.1.4          
## [79] timechange_0.2.0        ellipsis_0.3.2